# coefficients.pfr: Extract coefficient functions from a fitted pfr-object In refund: Regression with Functional Data

## Description

This function is used to extract a coefficient from a fitted 'pfr' model, in particular smooth functions resulting from including functional terms specified with `lf`, `af`, etc. It can also be used to extract smooths genereated using `mgcv`'s `s`, `te`, or `t2`.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25``` ```## S3 method for class 'pfr' coefficients( object, select = 1, coords = NULL, n = NULL, se = ifelse(length(object\$smooth) & select, TRUE, FALSE), seWithMean = FALSE, useVc = TRUE, Qtransform = FALSE, ... ) ## S3 method for class 'pfr' coef( object, select = 1, coords = NULL, n = NULL, se = ifelse(length(object\$smooth) & select, TRUE, FALSE), seWithMean = FALSE, useVc = TRUE, Qtransform = FALSE, ... ) ```

## Arguments

 `object` return object from `pfr` `select` integer indicating the index of the desired smooth term in `object\$smooth`. Enter 0 to request the raw coefficients (i.e., `object\$coefficients`) and standard errors (if `se==TRUE`). `coords` named list indicating the desired coordinates where the coefficient function is to be evaluated. Names must match the argument names in `object\$smooth[[select]]\$term`. If `NULL`, uses `n` to generate equally-spaced coordinates. `n` integer vector indicating the number of equally spaced coordinates for each argument. If length 1, the same number is used for each argument. Otherwise, the length must match `object\$smooth[[select]]\$dim`. `se` if `TRUE`, returns pointwise standard error estimates. Defaults to `FALSE` if raw coefficients are being returned; otherwise `TRUE`. `seWithMean` if `TRUE` the standard errors include uncertainty about the overall mean; if `FALSE`, they relate purely to the centered smooth itself. Marra and Wood (2012) suggests that `TRUE` results in better coverage performance for GAMs. `useVc` if `TRUE`, standard errors are calculated using a covariance matrix that has been corrected for smoothing parameter uncertainty. This matrix will only be available under ML or REML smoothing. `Qtransform` For additive functional terms, `TRUE` indicates the coefficient should be extracted on the quantile-transformed scale, whereas `FALSE` indicates the scale of the original data. Note this is different from the `Qtransform` arguemnt of `af`, which specifies the scale on which the term is fit. `...` these arguments are ignored

## Value

a data frame containing the evaluation points, coefficient function values and optionally the SE's for the term indicated by `select`.

## Author(s)

Jonathan Gellar and Fabian Scheipl

## References

Marra, G and S.N. Wood (2012) Coverage Properties of Confidence Intervals for Generalized Additive Model Components. Scandinavian Journal of Statistics.

refund documentation built on July 1, 2021, 9:06 a.m.